Embodiments presented herein relate generally to gas turbines, and particularly to online health monitoring of gas turbine compressors.
Gas turbines are widely employed in applications that require high output power while minimizing weight. Axial flow gas turbines are deployed in various applications such as auxiliary power units, industrial power plants, propulsion engines and so forth. The axial flow gas turbine typically includes a multistage compressor, a combustion chamber, and a single stage or multistage turbine.
Each stage of the multistage compressor includes a row of rotor blades followed by a row of stator blades. The working fluid flows through variable inlet guide vane (IGV) into the first stage of the compressor. The angle of the IGV controls the flow of the working fluid with the rotational speed of the rotor to improve the off-design performance of the gas turbine. In each stage, the rotor blades accelerate the working fluid. The working fluid then decelerates in the stator blade passages where the kinetic energy of the working fluid is converted into static pressure. The required overall pressure ratio is thus obtained by adding the required number of compressor stages. The process of conversion of kinetic energy to static pressure subjects the rotor blades and stator vanes to stress cycles. The stress cycles induce fatigue on the rotor and stator blades. The fatigue may lead to blade cracking, and subsequently blade liberation. Blade liberation usually leads to total failure of compressor.
Some known methods to detect compressor blade damage rely on periodic inspections of the compressor blades. Observations from the periodic inspections may then be used to run complex simulations to predict failure of the compressor blades. However, such methods may typically require the gas turbine to be shut down for inspection. Further, accurate simulations may require high computational capability and may not account for changes in operating conditions. Some other known methods rely on vibration measurements to detect anomalous vibrations. However, methods based on vibration measurements suffer from high rate of false alarms.
Therefore, there is a need for a system that provides accurate online detection of incipient failure of the compressor blades.